Big Data Planning a Course Toward Predictive Analytics

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Big Data – Planning a Course Toward

Predictive Analytics

A Complimentary Webinar From healthsystemCIO.com

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Agenda — 45 Minutes

• (20 min) David Miller, Vice Chancellor/CIO, University of Arkansas for

Medical Science

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“Big Data – Planning a Course

Toward Predictive Analytics”

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Presenter’s Background

Clinician (3 years) – Registered Medical Technologist

Hospital Operations (3 years) – Lead operations for hospital-based diabetes treatment centers

IT Vendor (4 years) – Financial decision support, cost accounting, budgeting, EIS, BI

IT and Management Consulting (15 years) – IT, process redesign/improvement, clinical transformation, strategic planning

Healthcare IT Leadership (9 years) – Lead 300-bed hospital, then #2 at one of the top academic medical centers in the nation

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Presenter’s Current Professional Roles

CHIME

CHIME national liaison to AHIMA – (September 2013 – Present) CHCIO Panel Reviewer (2011 – Present)

HIMSS

Committee Member, National HIE Committee - HIMSS (July 2013 – Present) President-elect, Arkansas HIMSS (July 2012 – Present)

Board Member and HIE Chair, Arkansas HIMSS (May 2011 – Present) AAMC

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Presenter’s Current Professional Roles

Other

Chair, HIE Council - Arkansas Office of Health Information Technology (June 2011 – Present)

Advisory Board Member - Pivot Point Consulting, LLC (October 2013 – Present)

Academic Medical Centers Advisory Council Expert - Next Wave Connect (October 2013 – Present)

Member, Information Technology Task Force - Novation (January 2013 – Present) Member, CIO Council – University Healthcare Consortium (August 2012 – Present)

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What are the challenges facing healthcare today?

We are being challenged by policy makers and society to:

• Bend the cost curve

• Increase quality

• Enhance patient safety

• Improve outcomes

• Shift to proactive care

• Effectively use IT

• Comply with government regulations

• Better educate future providers

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New Paradigm for Clinical Information Processing

Family History | Whole Genome | Clinical Data | Patient Reported | Monitoring

Algorithms

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Precision Medicine

“State-of-the-art molecular profiling to create diagnostic, prognostic,

and therapeutic strategies precisely tailored to each patient's

requirements.”

“The success of precision medicine will depend on establishing

frameworks for …interpreting the influx of information that can keep

pace with rapid scientific developments.”

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Genetic Testing Today

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Genetic Testing Today

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Other New Streams of Data

Over the next 3 years

• +1 billion smart phones will enter service

• 3 billion IP-enabled devices by 2015 By 2016

• 4.9 million patients will use remote health monitoring devices

• 3 million patients will use a remote monitoring device via a smartphone hub

• 142 million healthcare and medical app downloads The Healthcare Data Explosion

• Average person’s EHR ranges from 1 mb to 5 gb (based on age, etc.)

• 2012 US digitized patient data – 600 pedabytes to 10 exabytes (est.)

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Big Data Definition

“

Big Data

” is data whose scale, diversity, and complexity require new

architecture, techniques, algorithms, and analytics to manage it and

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- Ad-hoc querying and reporting - Data mining techniques

- Structured data, typical sources - Small to mid-size datasets

- Optimizations and predictive analytics - Complex statistical analysis

- All types of data, and many sources - Very large datasets

- More of a real-time

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Market trends driving health to Big Data

• Medical and health capabilities expanding.

• Changing demographics, expanding the need for more services.

• New care and reimbursement models emphasizing focus on

managing health across community and care settings.

• Exponential growth in health and medical information from a variety

of diverse sources.

• Health consumerism generating large amounts of unstructured data

through consumers’ participation in social media.

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Innovations Making Big Data Possible

• Increased use of electronic medical records (EMRs) and other digital data.

• New capabilities to combine and use of diverse data types from internal and external sources.

• Low-cost storage and process power.

• New software to handle speed and volume, structured and unstructured.

• Revolution of clinical user experience—right information at the right time, which improves decision support and care quality.

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Office of National Coordinator for Health

Information Technology

• Big data will revolutionize healthcare, says a new five-year strategic plan from the Office of the National Coordinator for Health Information Technology

• "Through a learning health system, the right information will be available to support a given decision, whether it is about the efficacy of a treatment or

medication for an individual patient, predicting a national pandemic, or deciding whether to proceed with the research and development for a potential new

treatment," the plan states.

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The Five Vs of Big Data

• Volume – quantity, from terabytes to zettabytes

• Variety – structured, unstructured, semi-structured

• Velocity – time-sensitive, real-time, predictive

• Veracity – quality, relevance, predictive value, meaningfulness

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Types of Big Data Value

• Treatment planning

• Health and social services continuity planning • Waste and fraud detection

• Increased awareness of consumer trends • Population health management

• Surveillance and health management • Improved research

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Examples of Big Data Value

• Access medical images from across the organization to speed patient diagnosis

• Capture and analyze physiological data in ICUs in real time to detect problems before they happen

• Integrate patient health information, patient preferences and insights from best practices and evidence generation.

• Continuously aggregate and analyze public health data to detect and manage potential outbreaks

• Analyze clinical data & claims for improved and more predictable outcomes

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Factors to Consider In Big Data Analytics

• BI Architecture – Optimized infrastructure (e.g., data marts, ODS)

• Data Sources – Web, patient, genomics, EMR – real time data extraction

• Types of Analysis/Use of Analytics – Analytics combining multiple and

complex data sources

• Data Models – TBD by each organization

• Data Governance - TBD by each organization

• Tools - TBD by each organization

• Skills needed - TBD by each organization

• Culture/enterprise data literacy - TBD by each organization

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Advanced Iterative Analytics

• Analytics on non-relational, multi-structured, machine-generated data

• Analytics that need to scale to big data sizes

• Analytics that require reorganization of data into new data structures –

graph, time & path analysis

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Keys to Success

• Never underestimate the importance of data quality as a foundation

• Make sure all the stakeholders are represented

• Understand the downstream impacts of data use & re-use

• Start with tools and models you are already familiar with

• Allow adequate time & resources to address governance,

accountability and stewardship

• Allow adequate time & resources to address data literacy across the

organization

• Understand the impact of patient and provider misidentification for

shared data

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Big Data Roadmap

Manage: • Data • Content • Streaming Information Integrate: Master Data Data Warehouse Analyze: • Content Analytics • Big Data • Cubes • Streams External Information Sources Business Analytics Applications Transactional & Collaborative Applications Govern

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The UAMS Journey - EDW

Phase 1 April, 2011 – October, 2011

• Data Sources targeted:

• Sunrise

• Logician

• Medipac

• Softlab Phase 1.5

• Physician billing data - Live: March, 2012 Phase 2 June, 2012 – Dec, 2012

• Data Sources targeted

• 6 AHEC EMRs

• Tissue Bank/Tumor Registry

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Available Data in EDW

Demographics

• Name

• Age

• Gender

• Zip Code (5-digit)

• Language

• Marital Status

• Race and Ethnicity

• Religion

Diagnoses (ICD-9 & CCS) { Sunrise, Centricity, Medipac } Laboratory tests MSDRG Discharge Disposition Visit Type Claims/Billing • Total Charges/Balance • Total Adjustments • Payment (Patient/Insurance) • Insurance Company

• Charge Code Description

• Cost Threshold

Provider

• Provider id (NPI, EIN etc)

• Provider Specialty

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Medications

• Sunrise Ordered

• Sunrise Administered

• Sunrise/Logician Prescribed

Procedures { Sunrise, Centricity, Medipac}

• CPT

• ICD-9/ICD-9 - CCS

• HCPCS

Vaccinations

• Ordered { Sunrise, Centricity }

• Administered { Sunrise, Centricity }

Vital Signs • Temperature • Pulse • BP Systolic • BP Diastolic • BMI Hospitalization

• LOS {by value}

• Admit Type • Emergency

• Elective

• Newborn

• Trauma

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Key Points:

1. We update the data twice monthly

2. There is significant monthly growth of data in existing source

systems

3. Different systems are the key sources of particular data types,

so as we bring new systems online, we better capture the

types.

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The UAMS Journey – Next Steps

• Data Governance Council – Spring, 2013

• Enterprise Epic Implementation – Complete by March, 2014

• Enterprise Implementation of SAP Business Objects – In process

• Molecular biology (genomics) lab implementation – In process

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Artificial Intelligence in Medicine

• Developing a search engine that will scan

thousands of medical records to turn up

documents related to patient queries.

• Learn based on how it is used

• “We are not contemplating ― unless this

were an unbelievably fantastic success ―

letting a machine practice medicine.”

http://www.health2news.com/2012/02/10/the

-national-library-of-medicine-explores-a-i/

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IBM Watson

• Medical records, texts, journals and research documents are all

written in natural language – a language that computers traditionally

struggle to understand. A system that instantly delivers a single,

precise answer from these documents could transform the

healthcare industry.

• “This is no longer a game”

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Q&A

Click on the Q&A panel located in the lower right corner of your screen,

type in your questions in the text field and hit send. Please keep the

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